- Machine Learning (Stanford CS 229 - Andrew Ng) - provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.
- EM (Richard Xu) - Proof of convergence for E-M, examples of E-M through Gaussian Mixture Model
- Machine Learning Lecture 12 (Stanford CS 229 - Andrew Ng) - discusses unsupervised learning in the context of clustering, Jensen's inequality, mixture of Gaussians, and expectation-maximization.
- Machine Learning (mathematicalmonk) Lecture 16.3
- Lecture 17 of the Introductory Applied Machine Learning (IAML) course - University of Edinburgh, by Victor lavrenko
- Intro to Artificial Intelligence (Sebastian Thrun & Peter Norvig) Unit 6
Hidden Markov Model
- HMM (Richard Xu) - Derivations for Kalman Filter and Hidden Markov Model
- Intro to Artificial Intelligence (Sebastian Thrun & Peter Norvig) Unit 11
- MCMC (Richard Xu) - Overview of several Sampling techniques, including Rejection, Adaptive Rejection, Importance, Markov Chain Monte Carlo (MCMC), Gibbs, Bootstrap Particle Filter, and Auxiliary Particle Filter
- VI (Richard Xu) - Explain Variational Bayes in two parts: non-exponential and exponential family distribution plus stochastic variational inference.
- DP (Richard Xu) - Dirichlet Process, Hieratical Dirichlet Process, HDP-HMM, Indian Buffet Process, and applications of DP to relational models.